26 to 50 of 852 Machine Learning Jobs in London

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

classification, forecasting, anomaly detection) Supporting experimentation cycles: hypothesis definition, testing, and evaluation Contributing to the deployment of models into production environments Building and maintaining ML pipelines using AWS services Assisting in the development of GenAI/LLM-based applications (e.g. prompt engineering, RAG pipelines) Implementing best practices in experiment tracking … lifecycle Required Skills & Experience Minimum 1+ year of commercial experience in machine learning or data science Strong Python skills with experience in ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost Exposure to AWS services (e.g. S3, Lambda, SageMaker) Understanding of core ML concepts: model evaluation, validation ...

models that make sense of high-dimensional biological data. The successful candidate will work alongside bioinformaticians, software engineers, and lab scientists to build scalable ML systems that directly influence the discovery pipeline. This is a hands-on role with ownership - from prototyping models to productionising them in a fast-moving … teams to translate experimental data into actionable insights Optimise models for performance, scalability, and real-world usability Contribute to the design of their ML infrastructure and tooling stack What They’re Looking For 4+ years experience in machine learning, data science, or AI engineering Strong Python skills (NumPy ...

Senior ML Engineer £65000 GBP Hybrid WORKING Location: Central London, Greater London - United Kingdom Type: Permanent Machine Learning Engineer Gloucester | Up to £60,000 + 10% Bonus + £7,000 DV Allowance A leading organisation operating within cyber, defence, and national security is seeking a Machine Learning Engineer to join a growing advanced analytics and AI capability. This is a hands-on engineering role building and deploying ML solutions in highly secure, data-rich environments , where work directly contributes to national-level outcomes. Why this role? This is an opportunity to work on real-world, high ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments … full lifecycle of machine learning, from model development, deployment and serving to monitoring and maintenance. Strong proficiency in Python and knowledge of ML libraries/frameworks (e.g., TensorFlow, PyTorch). . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving ...

space sectors Explore and implement machine learning models on complex, multi-modal datasets Influence technical direction and best practice in AI, ML, and statistics Mentor and support junior data scientists and graduates Engage directly with clients to scope, design, and deliver scientifically-justified solutions Key Responsibilities … technical lead and trusted advisor for AI and data science expertise across projects and bids Lead and contribute to AI and ML projects, collaborating with ML engineers, project managers, and non-technical stakeholders Meet with clients to understand their challenges, scope projects, and plan delivery of AI solutions Support ...

with our hardware. Experience: Proven track record of developing novel algorithms (papers in NeurIPS, ICML, ICLR, CVPR, or Nature/Science journals) Hardware Aware ML/Neuromorphic Computing: FPGAs, ASICs, analog computing chips, spiking neural network (hardware), edge AI Unconventional training algorithms Reservoir computing, self-constrastive learning, forward-forward … learning, evolutionary algorithms, equilibrium propagation Physics informed neural networks, or applied ML to physics problems Deep understanding of mathematics, algebra/topology - key words are latent space, intrinsic dimensionality Experience working with hardware as well is a bonus ...

Overview We are looking for exceptional machine learning engineers to work alongside our quantitative researchers on cutting-edge machine learning problems. As a member of the Core Technical Machine Learning team, you will be engaged in a mixture of individual and collaborative work … Writing custom libraries for efficiently training on petabytes of data Reducing model training times by hand optimising machine learning operations Profiling custom ML architectures to identify performance bottlenecks Evaluating the latest hardware and software in the machine learning ecosystem #J-18808-Ljbffr ...

parts of the supply chain—enabling bulk inventory exchange, streamlined digital payments, and generating vast quantities of aggregated market data. By applying AI and machine learning techniques, we deliver predictive market insight and trading recommendations straight back to the trading environment—facilitating smarter everyday business decisions … align data strategy with broader business goals. You will play an integral role, taking ownership of a team focused on designing, developing, and deploying ML systems to solve real world problems and enhance business processes, leveraging cloud platforms to build, train, and scale ML models. As Head of Data Science ...

Partner with Platform and Commercial leaders to identify where intelligence can unlock step-change improvements. Bring a clear point of view on AI investment, ML investment, experimentation evolution and long-term capability. Bring a clear playbook for build vs buy decisions in an evolving vendor space. Deliver real-world impact … Lead the development and productionisation of ML systems across search, pricing, recommendation, marketing optimisation and elsewhere. Drive measurable value through direct delivery and by scaling ML capability across the organisation. Ensure our experimentation and modelling approaches are rigorous, scalable and commercially grounded. Translate complex technical work into clear business insight ...

hardest problems. Accountability for every result. Integrity always. About The Role Lead the architecture and evolution of scalable, high-performance data pipelines and ML systems , focusing on data ingestion, transformation, quality checks, and enrichment. Provide technical leadership and mentorship to a cross-functional team of ML Engineers, Data Scientists … automate data quality, link canonical products, and create intelligent data enrichment solutions. Define strategies to enhance the performance, reliability, and observability of data and ML services, ensuring robust, high-quality data outputs. Design and implement frameworks for evaluating data quality and the effectiveness of ML models through both offline metrics ...

fast and accurate results • Designing and implementing agentic AI workflows powered by LLMs • Building and maintaining scalable data pipelines • Leading technical decision-making across ML and NLP projects Requirements • 8+ years of commercial engineering and/or data science experience • Experience in technical leadership or leading ML initiatives • Strong Python … programming skills • Commercial experience with machine learning • Experience working in Natural Language Processing (NLP) • Experience training ML models using TensorFlow and/or PyTorch • Experience with search databases (e.g. Elasticsearch, OpenSearch) • Experience with cloud platforms (e.g. AWS) • Willingness to work 3 days per week onsite in London Benefits ...

/Developer (Python Machine Learning AWS) Remote UK to £90k Are you a data savvy Backend Software Engineer with experience of implementing ML algorithms into production? You could be progressing your career in a senior, hands-on Backend Software Engineer role as part of a friendly and supportive … About you: You are a data savvy Backend Software Engineer with advanced Python coding skills You have experience of across the full lifecycle of ML model development including into production You're collaborative, enjoy problem solving and working with others to overcome technical challenges You have a strong knowledge ...

machine learning models end to end. Experience with LLMs, NLP, conversational AI, RAG pipelines and model fine tuning. Understanding of production grade ML workflows and CI CD principles. Experience building chatbots or NLP driven chat engines used in live environments. Comfortable working independently and taking ownership of complex … structure with senior mentorship and clear technical progression. How to Apply If you are a Senior Data Scientist looking to work on production level ML and LLM solutions within financial services, apply now to find out more. ...

finance-driven analytics role. The focus is on scientific, engineering, and operational data, including time-series and image data, with exposure to machine learning experimentation and modern AI tooling, including GenAI. What You’ll Be Doing Data Preparation and Engineering Collect, clean, and validate data from sensors, internal … issues. Exploratory Analysis and Insight Generation Perform exploratory data analysis to identify trends, anomalies, and patterns. Translate findings into clear, structured insights for stakeholders. Machine Learning and AI Support Support development and testing of machine learning pipelines. Work with time-series, tabular, and image datasets. Assist ...